Active learning

In this section, we will explore the concept of active learning and its application in data labeling. Active learning is a powerful technique that allows us to label data more efficiently by actively selecting the most informative samples for annotation. By strategically choosing which samples to label, we can achieve higher accuracy with a smaller dataset, all else being equal. On the following pages, we will discuss various active learning strategies and implement them using Python code examples.

Active learning is a semi-supervised learning approach that involves iteratively selecting a subset of data points for manual annotation based on their informativeness. The key idea is to actively query the labels of the most uncertain or informative instances to improve the learning process. This iterative process of selecting and labeling samples can significantly reduce the amount of labeled data required to achieve the desired level of performance.

Let’s...